Repositorio de producción científica de la Universidad de Sevilla

A Non-parametric Approach for Accurate Contextual Classification of LIDAR and Imagery Data Fusion

Opened Access A Non-parametric Approach for Accurate Contextual Classification of LIDAR and Imagery Data Fusion

Citas

buscar en

Estadísticas
Icon
Exportar a
Autor: García Gutiérrez, Jorge
Mateos García, Daniel
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2012
Publicado en: Hybrid Artificial Intelligent Systems :7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012. Proceedings, Part II. Lecture Notes in Computer Science, v.7209
ISBN/ISSN: 978-3-642-28930-9
Tipo de documento: Capítulo de Libro
Resumen: Light Detection and Ranging (LIDAR) has become a very important tool to many environmental applications. This work proposes to use LIDAR and image data fusion to develop high-resolution thematic maps. A novel methodology is presented which starts building a matrix of statistics from spectral and spatial information by feature extraction on the available bands (RGB from images, and intensity and height from LIDAR). Then, a contextual classification is applied to generate the final map using a support vector machine (SVM) to classify every cell and the nearest neighbor (NN) rule to sequentially reclassify each cell. The results obtained by this novel method, called SVMNNS (SVM and NN Stacking), are compared with non-contextual and contextual SVMs. It is shown that SVMNNS obtains the best results when applied to real data from the Iberian peninsula.
Tamaño: 554.9Kb
Formato: PDF

URI: http://hdl.handle.net/11441/42648

DOI: http://dx.doi.org/10.1007/978-3-642-28931-6_44

Mostrar el registro completo del ítem


Esta obra está bajo una Licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional

Este registro aparece en las siguientes colecciones